Why the same LLM gives different answers in different environments
The article discusses how different environments can influence the responses generated by language models. It highlights a case where one environment provided a complete answer while another offered additional context that revealed underlying issues. This phenomenon, termed Ambient Frame Retrieval Bias, occurs before the retrieval process begins, affecting how questions are interpreted.
- ▪The author explores a concept called Phantom Resolution, where unresolved questions are treated as resolved.
- ▪Two environments, ide and desk, were used to illustrate how context influences the retrieval of information.
- ▪The article identifies a pre-retrieval layer called Ambient Frame Retrieval Bias that shapes how questions are parsed.
Opening excerpt (first ~120 words) tap to expand
The Environment Rewrites the Question Before I Ask ItWhat I found diagnosing a failure mode in my own system, and the moment retrieval turned out to be already shaped before it startedJohn WadeApr 28, 2026ShareI was teaching one of my environments a concept from its own knowledge base. The concept is called Phantom Resolution — a failure mode where a question that hasn't actually been resolved gets treated as if it has. I had built the concept myself, months earlier, while watching a different failure pattern repeat itself across sessions. It was a good concept. It had structure, examples, a clean definition.The environment I was teaching — I'll call it ide, the one where I write code and run infrastructure — gave back a textbook-correct answer.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Substack.
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